Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
📰 ArXiv cs.AI
Learn to model explanations with annotator-specific rationales for fine-grained perspectives in NLI tasks
Action Steps
- Collect a dataset with disaggregated NLI annotations and annotator-provided explanations
- Preprocess the data by tokenizing and formatting the explanations
- Fine-tune a pre-trained model on the annotators' provided rationales
- Jointly model annotator-specific label prediction and corresponding explanations
- Evaluate the model using metrics such as accuracy and explanation quality
Who Needs to Know This
NLP researchers and ML engineers can benefit from this approach to improve model interpretability and performance
Key Insight
💡 Annotator-specific rationales can provide fine-grained signals of individual perspectives, improving model performance and interpretability
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📚 Improve NLP model interpretability with annotator-specific rationales! 🤖
Key Takeaways
Learn to model explanations with annotator-specific rationales for fine-grained perspectives in NLI tasks
Full Article
Title: Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
Abstract:
arXiv:2604.21667v1 Announce Type: cross Abstract: Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on the annotators' provided rationales. Using a dataset with disaggregated natural language inference (NLI) annotations and annotator-provided explanations, we condition
Abstract:
arXiv:2604.21667v1 Announce Type: cross Abstract: Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on the annotators' provided rationales. Using a dataset with disaggregated natural language inference (NLI) annotations and annotator-provided explanations, we condition
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